Hyperion-2.0-Mistral-7B Open-Source Language Model - Free Deployment to Assist in Scientific Reasoning and Complex Tasks

Hyperion 2.0 Mistral 7B

Developed by Locutusque

A multi-domain language model fine-tuned on the Hyperion-v2.0 dataset, excelling in scientific reasoning and complex task processing.

Large Language Model

Transformers

Supports Multiple LanguagesOpen Source License:Apache-2.0 #Scientific Reasoning #Cross-domain Question Answering #Medical Text Understanding

Downloads 16

Release Time : 3/10/2024

Model Overview

This model is an advanced language model based on the Mistral-7B architecture, fine-tuned for complex reasoning tasks in science, medicine, mathematics, and programming.

Model Features

Multi-domain Scientific Reasoning

Integrates knowledge from programming, medicine, mathematics, and other fields, excelling in handling interdisciplinary complex problems

Advanced Instruction Understanding

Capable of understanding and executing complex queries and instructions, suitable for professional scenarios

Quantized Version Support

Provides multiple quantized versions like ExLlamaV2, GGUF, and AWQ for easy deployment on different hardware

Model Capabilities

Complex Question Answering

Conversational Understanding

Code Generation

Medical Text Understanding

Mathematical Reasoning

Logical Reasoning

Use Cases

Education

Science Tutoring System

Serves as an AI-driven tutoring tool for science, medicine, and mathematics

Professional Assistance

Domain Information Retrieval

Quickly retrieves professional information in scientific and technical fields

Development

Code Generation

Understands complex programming contexts and generates code

🚀 Hyperion-2.0-Mistral-7B

A state-of-the-art language model for advanced reasoning across scientific domains

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📚 Documentation

Model Details

Property	Details
Model Name	Locutusque/Hyperion-2.0-Mistral-7B
Base Model	mistralai/Mistral-7B-v0.1
Publisher	Locutusque
Model Type	Question answering, conversational AI, code generation, medical text comprehension, mathematical reasoning, logical reasoning.
Language	Multi-domain, English language.
License	Apache-2.0

Model Description

Locutusque/Hyperion-2.0-Mistral-7B is a state-of-the-art language model fine-tuned on the Hyperion-v2.0 dataset for advanced reasoning across scientific domains. This model is designed to handle complex inquiries and instructions, leveraging the diverse and rich information contained in the Hyperion dataset. Its primary use cases include but are not limited to complex question answering, conversational understanding, code generation, medical text comprehension, mathematical reasoning, and logical reasoning.

Intended Use

This model is intended for researchers and practitioners looking for a powerful tool to tackle challenging problems in scientific domains. It can be used in the following scenarios:

AI-driven tutoring systems for science, medicine, mathematics, and computer science.
Assistive tools for professionals requiring fast and accurate domain-specific information retrieval.
Platforms that require conversational AI capabilities with a focus on technical and scientific reasoning.
Automation in code generation and understanding complex programming context.

Training Data

The Locutusque/Hyperion-2.0-Mistral-7B model was fine-tuned on 750,000 examples of the Hyperion-v2.0 dataset, which amalgamates various datasets rich in diversity and complexity, including programming, medical texts, mathematical problems, and reasoning tasks.

Quants

ExLlamaV2: https://huggingface.co/bartowski/Hyperion-2.0-Mistral-7B-exl2
GGUF: https://huggingface.co/bartowski/Hyperion-2.0-Mistral-7B-GGUF
AWQ: https://huggingface.co/solidrust/Hyperion-2.0-Mistral-7B-AWQ

Evaluation Results

0-shot AGIEval

Tasks	Version	Filter	Metric	Value		Stderr
agieval_nous	N/A	none	acc	0.3602	±	0.0929
		none	acc_norm	0.3342	±	0.0764
- agieval_aqua_rat	1	none	acc	0.2402	±	0.0269
		none	acc_norm	0.2441	±	0.0270
- agieval_logiqa_en	1	none	acc	0.2965	±	0.0179
		none	acc_norm	0.3226	±	0.0183
- agieval_lsat_ar	1	none	acc	0.2348	±	0.0280
		none	acc_norm	0.2000	±	0.0264
- agieval_lsat_lr	1	none	acc	0.3667	±	0.0214
		none	acc_norm	0.3373	±	0.0210
- agieval_lsat_rc	1	none	acc	0.4981	±	0.0305
		none	acc_norm	0.4089	±	0.0300
- agieval_sat_en	1	none	acc	0.6359	±	0.0336
		none	acc_norm	0.5777	±	0.0345
- agieval_sat_en_without_passage	1	none	acc	0.3883	±	0.0340
		none	acc_norm	0.3544	±	0.0334
- agieval_sat_math	1	none	acc	0.3500	±	0.0322
		none	acc_norm	0.2682	±	0.0299

Groups

Groups	Version	Filter	n-shot	Metric	Value		Stderr
agieval_nous	N/A	none	0	acc	0.3602	±	0.0929
		none	0	acc_norm	0.3342	±	0.0764

5-shot AGIEval coming soon.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Locutusque/Hyperion-1.5-Mistral-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# For a text generation task
input_text = "<|im_start|>user\nWhat are the implications of Einstein's theory of relativity in modern physics?<|im_end|>\n<|im_start|>assistant\n"
input_ids = tokenizer.encode(input_text, return_tensors="pt")

# Generate a response
outputs = model.generate(input_ids, max_length=200, num_return_sequences=1, temperature=0.8, top_p=0.95, top_k=40, repetition_penalty=1.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🔧 Technical Details

Known Limitations

⚠️ Important Note

The diversity of the dataset could lead to inconsistencies in the model's responses due to variations in data formatting and annotation quality.

💡 Usage Tip

This model is also very compliant, it will respond to any request. Please make sure to build upon this model with DPO if you plan on using it for enterprise-level deployment.

📄 License

This model is released under the Apache-2.0 license.

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